Stuart, A. M. (2010) The hybrid Monte Carlo algorithm on Hilbert space. In: Stochastic Partial Differential Equations (SPDEs) : Approximation, Asymtotics and Computation. , Cambridge, 28 Jun-02 Jul 2010 (Unpublished)

Research output not available from this repository, contact author.

Request Changes to record.

Abstract

Hybrid Monte Carlo methods are a class of algorithms for sampling probability measures defined via a density with respect to Lebesgue measure. However, in many applications the probability measure of interest is on an infinite dimensional Hilbert space and is defined via a density with respect to a Gaussian measure. I will show how the Hybrid Monte Carlo methodology can be extended to this Hilbert space setting. A key building block is the study of measure preservation properties for certain semilinear partial differential equations of Hamiltonian type, and approximation of these equations by volume-preserving integrators. Joint work with A. Beskos (UCL), F. Pinski (Cincinnati) and J.-M. Sanz-Serna (Valladolid).

Item Type:	Conference Item (Paper)
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Science > Mathematics
Official Date:	30 June 2010
Dates:	Date Event 30 June 2010 Completion
Status:	Not Peer Reviewed
Publication Status:	Unpublished
Version or Related Resource:	Stuart, A. M. (2010). The hybrid Monte Carlo algorithm on Hilbert space. In: Highly Oscillatory Problems : From Theory to Applications. Cambridge, 15 Sep 2010.
Conference Paper Type:	Paper
Title of Event:	Stochastic Partial Differential Equations (SPDEs) : Approximation, Asymtotics and Computation.
Type of Event:	Conference
Location of Event:	Cambridge
Date(s) of Event:	28 Jun-02 Jul 2010

Request changes or add full text files to a record

Repository staff actions (login required)

View Item